Data to 7 Oct 2026

Multi-Objective Deep Reinforcement Learning

arXiv 1610.02707 ↗First posted 9 Oct 2016cs.AIcited by 81

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Abstract (arXiv)

We propose Deep Optimistic Linear Support Learning (DOL) to solve high-dimensional multi-objective decision problems where the relative importances of the objectives are not known a priori. Using features from the high-dimensional inputs, DOL computes the convex coverage set containing all potential optimal solutions of the convex combinations of the objectives. To our knowledge, this is the first time that deep reinforcement learning has succeeded in learning multi-objective policies. In addition, we provide a testbed with two experiments to be used as a benchmark for deep multi-objective reinforcement learning.

Key facts. Cited by 81 papers in the map (data through 7 Oct 2026). First posted on arXiv 9 Oct 2016.

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Citing papers per year: 2019: 1; 2020: 0; 2021: 9; 2022: 14; 2023: 17; 2024: 16; 2025: 13; 2026: 11.

81 papers.

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2021A Practical Guide to Multi-Objective Reinforcement Learning and Planningcited by 240
2023Rewarded soups: towards Pareto-optimal alignment by interpolating weights fine-tuned on diverse rewardscited by 232
2022PD-MORL: Preference-Driven Multi-Objective Reinforcement Learning Algorithmcited by 51
2023Sample-Efficient Multi-Objective Learning via Generalized Policy Improvement Prioritizationcited by 46
2021Learning Controllable Content Generatorscited by 40
2023Scaling Pareto-Efficient Decision Making Via Offline Multi-Objective RLcited by 36
2024Parrot: Pareto-optimal Multi-Reward Reinforcement Learning Framework for Text-to-Image Generationcited by 35
2023Multi-Objective Reinforcement Learning Based on Decomposition: A Taxonomy and Frameworkcited by 28
2023Promptable Behaviors: Personalizing Multi-Objective Rewards from Human Preferencescited by 25
2025CAPO: Towards Enhancing LLM Reasoning through Generative Credit Assignmentcited by 25
2021Provably Efficient Cooperative Multi-Agent Reinforcement Learning with Function Approximationcited by 23
2022Pareto Conditioned Networkscited by 22

38 more

2022Optimistic Linear Support and Successor Features as a Basis for Optimal Policy Transfercited by 22
2021Levels of explainable artificial intelligence for human-aligned conversational explanationscited by 21
2022Imitating Past Successes can be Very Suboptimalcited by 20
2022PrefixRL: Optimization of Parallel Prefix Circuits using Deep Reinforcement Learningcited by 18
2025Pareto Set Learning for Multi-Objective Reinforcement Learningcited by 18
2023Two-Stage Constrained Actor-Critic for Short Video Recommendationcited by 16
2021Choosing the Best of Both Worlds: Diverse and Novel Recommendations through Multi-Objective Reinforcement Learningcited by 14
2022Socially Fair Reinforcement Learningcited by 13
2022Policy Optimization with Advantage Regularization for Long-Term Fairness in Decision Systemscited by 13
2025Bone Soups: A Seek-and-Soup Model Merging Approach for Controllable Multi-Objective Generationcited by 13
2025Formalizing Learning from Language Feedback with Provable Guaranteescited by 10
2021Towards Content Provider Aware Recommender Systems: A Simulation Study on the Interplay between User and Provider Utilitiescited by 9
2024Policy-regularized Offline Multi-objective Reinforcement Learningcited by 9
2024Multi-objective Reinforcement learning from AI Feedbackcited by 9
2023Achieving Fairness in Multi-Agent Markov Decision Processes Using Reinforcement Learningcited by 8
2024Beyond Worst-case Attacks: Robust RL with Adaptive Defense via Non-dominated Policiescited by 8
2025VARP: Reinforcement Learning from Vision-Language Model Feedback with Agent Regularized Preferencescited by 8
2019Integration of Imitation Learning using GAIL and Reinforcement Learning using Task-achievement Rewards via Probabilistic Graphical Modelcited by 7
2022Behaviour-Diverse Automatic Penetration Testing: A Curiosity-Driven Multi-Objective Deep Reinforcement Learning Approachcited by 7
2024Multi-objective Deep Learning: Taxonomy and Survey of the State of the Artcited by 6
2026MO-MIX: Multi-Objective Multi-Agent Cooperative Decision-Making With Deep Reinforcement Learningcited by 6
2023Multi-Objective Reinforcement Learning-based Approach for Pressurized Water Reactor Optimizationcited by 5
2024An Offline Adaptation Framework for Constrained Multi-Objective Reinforcement Learningcited by 5
2024How to Find the Exact Pareto Front for Multi-Objective MDPs?cited by 5
2021On Multi-objective Policy Optimization as a Tool for Reinforcement Learning: Case Studies in Offline RL and Finetuningcited by 4
2022Multi-objective Optimization of Notifications Using Offline Reinforcement Learningcited by 4
2025Scalable Multi-Objective Robot Reinforcement Learning through Gradient Conflict Resolutioncited by 4
2021Preference Communication in Multi-Objective Normal-Form Gamescited by 3
2023Deep Reinforcement Learning from Hierarchical Preference Designcited by 3
2022Multi-Objective reward generalization: Improving performance of Deep Reinforcement Learning for applications in single-asset tradingcited by 2
2022Constrained Reinforcement Learning for Short Video Recommendationcited by 2
2023Pruning the Way to Reliable Policies: A Multi-Objective Deep Q-Learning Approach to Critical Carecited by 2
2024Demonstration Guided Multi-Objective Reinforcement Learningcited by 2
2024A Meta-Learning Approach for Multi-Objective Reinforcement Learning in Sustainable Home Environmentscited by 2
2025Dual-Objective Reinforcement Learning with Novel Hamilton-Jacobi-Bellman Formulationscited by 2
2025Multi-Objective Bilevel Learningcited by 2
2026LAD: Learning Advantage Distribution for Reasoningcited by 2
2026A Reward-Free Viewpoint on Multi-Objective Reinforcement Learningcited by 2

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